Objectives

Using the spatial visualization techniques, explore this data set on Pennsylvania hospitals (http://www.arcgis.com/home/item.html?id=eccee5dfe01e4c4283c9be0cfc596882). Create a series of 5 maps that highlight spatial differences in hospital service coverage for the state of PA.

To help you in getting the data imported into R, I have included the code below:

To import the data I use the foreign package, if you do not have it than be sure to install it prior to testing the code.

library(foreign)
library(ggmap)
## Loading required package: ggplot2
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
library(ggplot2)
dat <- read.dbf("pennsylv.dbf")  # change this to connect to your version
register_google(key = "AIzaSyBKWLYOan936_QqwfP224GN6u8he-Djhho")

The dataset contains a number of variables about each hospital, many of them are clear and straight forward.

names(dat)
##   [1] "acc_trauma" "air_amb"    "als"        "arc_street" "arc_zone"  
##   [6] "bas_ls"     "bassinets"  "bb_id"      "bc_beds"    "bc_sus_bed"
##  [11] "beds_sus"   "birthing_r" "bone_marro" "burn_car"   "burn_care" 
##  [16] "card_beds"  "card_surge" "card_sus_b" "cardiac"    "cardiac_ca"
##  [21] "cardio_reh" "chemo"      "city"       "clin_lab"   "clin_psych"
##  [26] "county"     "countyname" "ct_scan"    "cty_key"    "cystoscopi"
##  [31] "deliv_rms"  "dental"     "detox_alc_" "diag_radio" "diag_xray" 
##  [36] "doh_hosp"   "doh_phone"  "emer_dept"  "endoscopie" "fac_id"    
##  [41] "facility"   "flu_old"    "fred_con_1" "fred_conta" "fred_email"
##  [46] "fred_fax"   "fred_hosp"  "fred_pager" "fred_phone" "gamma_knif"
##  [51] "gen_outpat" "gene_counc" "heart_tran" "helipad"    "hemodial_c"
##  [56] "hemodial_m" "hosp_id"    "hospice"    "hyper_cham" "icu"       
##  [61] "icu_beds"   "icu_sus_be" "inpat_flu_" "inpat_pneu" "kidney_tra"
##  [66] "labor_rms"  "lic_beds"   "lic_dent"   "lic_dos"    "lic_mds"   
##  [71] "lic_pod"    "linear_acc" "lithotrips" "liver_tran" "loc_method"
##  [76] "ltc"        "mcd"        "mcd_key"    "mcd_name"   "medical"   
##  [81] "mob_ccu"    "mob_icu"    "mri"        "ms1"        "neo2_beds" 
##  [86] "neo2_sus_b" "neo3_beds"  "neo3_sus_b" "neuro_surg" "neurology" 
##  [91] "obs_gyn"    "occ_ther"   "optometry"  "organ_bank" "ped_trauma"
##  [96] "pediatric"  "pet"        "pharmacy"   "phys_med"   "phys_ther" 
## [101] "podiatry"   "providerid" "psych"      "psych_inpa" "reg_trauma"
## [106] "resp_ther"  "so_flu_65u" "social_wor" "speech_pat" "street"    
## [111] "surgical"   "surgical_s" "thera_radi" "typ_org"    "typ_serv"  
## [116] "ultrasound" "x"          "y"          "zip"

Now create 5 maps, including descriptions, that highlight the spatial distribution of hospital services in the state of PA. Upload these maps as a document to rpubs.com and submit that link the Moodle assignment.

1. Hospitals in PA state that provides radiology services

radio = subset(dat, diag_radio == "Y")
qmplot(x, y, data = radio, zoom = 7,source = 'google', maptype = 'roadmap',  colour= I('red'), mapcolor = "color", extent = "panel", main = "Hospitals in PA state that provides radiology services", xlab = "Longitude", ylab = "Latitude")
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942648,-77.681596&zoom=7&size=640x640&scale=2&maptype=roadmap&language=en-EN&key=xxx-Djhho

From the plot above, we found that the red spots are mainly accumulated in two major areas in PA state - Philadelphia and Pittsburgh. Ohters are scattered along with the major highway road.

2. Hospital with dental service in greater Philadelpia area

dental<-subset(dat, dental == "Y"&county=="Philadelphia")
qmplot(x, y, data = dental, zoom = 11,source = "google", maptype = "toner-2011",  colour= I('orange'), , size = I(3),mapcolor = "color", extent = "panel", main = "Hospital with dental service in greater Philadelpia area", xlab = "Longitude", ylab = "Latitude")
## maptype = "toner-2011" is only available with source = "stamen".
## resetting to source = "stamen"...
## Source : https://maps.googleapis.com/maps/api/staticmap?center=39.993566,-75.162372&zoom=11&size=640x640&scale=2&maptype=terrain&key=xxx-Djhho
## Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
## Warning: Ignoring unknown parameters: NA

There are not too many selections for dental services in Philadelphia. The chart above shows six locations in this area that providing dental services.

3. Hospital beds density in PA state

qmplot(x, y, data = dat, size = beds_sus, source="google",maptype = "satellite", zoom=7, color = I("yellow"), extent = "panel",main = "Hospital beds density in PA state", xlab = "Longitude", ylab = "Latitude")
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942826,-77.681596&zoom=7&size=640x640&scale=2&maptype=satellite&language=en-EN&key=xxx-Djhho
## Warning: Removed 31 rows containing missing values (geom_point).

The chart above that within PA state, the hospitals with more than 1000 beds provided are mainly in Philadelphia and Pittsburgh.

4. Distribution of hospital services across PA state

qmplot(x, y, data = dat, size = I(3),colour=typ_serv,extent = "panel", main = "Distribution of hospital services across PA state", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "toner-lite",zoom = 7)
## maptype = "toner-lite" is only available with source = "stamen".
## resetting to source = "stamen"...
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.942826,-77.681596&zoom=7&size=640x640&scale=2&maptype=terrain&key=xxx-Djhho
## Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

5. For hospitals that specialized in Ob-Gyn, whether they have generic counseling for women’s pregnancy

ob<-subset(dat,obs_gyn=="Y")
qmplot(x, y, data = ob, size = I(3),colour=gene_counc,extent = "panel", main = "Generic counseling services for hospitals specialized in Ob-Gyn", xlab = "Longitude", ylab = "Latitude",source = "google",maptype = "watercolor",zoom = 7)
## maptype = "watercolor" is only available with source = "stamen".
## resetting to source = "stamen"...
## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.764134,-77.585179&zoom=7&size=640x640&scale=2&maptype=terrain&key=xxx-Djhho
## Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under CC BY SA.

It seems that wihin the 46 hospitals that have speciaty in Ob-Gyn, only some of them have generic counseling services.